Welfare cost of business cycles in economies with individual consumption risk

We have gone three weeks without a NEP-DGE report, plus this week’s report was much larger than usual. A good reason to pick a second paper.

By Martin Ellison and Thomas Sargent

http://d.repec.org/n?u=RePEc:hhs:bofrdp:2012_025&r=dge

The welfare cost of random consumption fluctuations is known from De Santis (2007) to be increasing in the level of individual consumption risk in the economy. It is also known from Barillas et al. (2009) to increase if agents in the economy care about robustness to model misspecification. In this paper, we combine these two effects and calculate the cost of business cycles in an economy with consumers who face individual consumption risk and who fear model misspecification. We find that individual risk has a greater impact on the cost of business cycles if agents already have a preference for robustness. Correspondingly, we find that endowing agents with concerns about a preference for robustness is more costly if there is already individual risk in the economy. The combined effect exceeds the sum of the individual effects.

It has proven quite difficult to find a theory that justifies large costs from business cycle fluctuations, unless one chooses unreasobaly high risk aversion, for example. Adding individual risk has been moderately successful, in large part because business cycles amplify little individual risk. But this paper shows that if you add in model uncertainty, household become really worried about business cycles and one can find substantial costs from aggregate fluctuations.

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Efficient Simulation of DSGE Models with Inequality Constraints

By Tom Holden and Michael Paetz

http://d.repec.org/n?u=RePEc:ham:qmwops:21207b&r=dge

This paper presents a fast, simple and intuitive algorithm for simulation of linear dynamic stochastic general equilibrium models with inequality constraints. The algorithm handles both the computation of impulse responses, and stochastic simulation, and can deal with arbitrarily many bounded variables. To illustrate the usefulness and efficiency of this algorithm we provide two applications according to the zero lower bound (ZLB) on nominal interest rates. Our solution principle is much faster than comparable methods. We therefore expect this algorithm to be very helpful also for estimation procedures, and for a wide range of applications apart from monetary policy analysis.

At least since some countries have been stuck at close to zero interest rates, inequality constraints have become an important issue in macroeconomics. Of course, model solutions based on linearization cannot take inequality conditions, and this paper shows a way around the issue that an in particular be implemented in DYNARE. But I remain unconvinced that this beats a higher order approximation.